Environmental variables considered for building of regression models of fish species richness in Teesta river.

<p>Environmental variables considered for building of regression models of fish species richness in Teesta river.</p

Fish species richness plots along the elevational gradient in the Himalaya.

<p>(A) total species richness (n = 179), non endemic species richness (n = 150) and endemic species richness (n = 29). (B) Species richness plots along the elevational gradient in the Teesta river. The fitted lines for total richness, non endemic richness in the Himalayan rivers and total fish species richness in the Teesta river represent a GAM model. However, for the endemic species of the Himalayan rivers the fitted line represents GLM model.</p

The significant variables, which effected maximum changes in percentage D² values.

<p>(A) Water temperature represents physico-chemical model. (B) Phytoplankton density represents biological model. (C) Water discharge represents the physiographic model. (D) Basin area represents the topographic model. Discharge was the most important determining factor of fish species richness pattern followed by basin area and water temperature in decreasing order.</p

Summary statistics for the selection of model variables.

<p>Null deviance = 88.77; d.f. = 8.</p

Elevational Gradients in Fish Diversity in the Himalaya: Water Discharge Is the Key Driver of Distribution Patterns

<div><h3>Background</h3><p>Studying diversity and distribution patterns of species along elevational gradients and understanding drivers behind these patterns is central to macroecology and conservation biology. A number of studies on biogeographic gradients are available for terrestrial ecosystems, but freshwater ecosystems remain largely neglected. In particular, we know very little about the species richness gradients and their drivers in the Himalaya, a global biodiversity hotspot.</p> <h3>Methodology/Principal Findings</h3><p>We collated taxonomic and distribution data of fish species from 16 freshwater Himalayan rivers and carried out empirical studies on environmental drivers and fish diversity and distribution in the Teesta river (Eastern Himalaya). We examined patterns of fish species richness along the Himalayan elevational gradients (50–3800 m) and sought to understand the drivers behind the emerging patterns. We used generalized linear models (GLM) and generalized additive models (GAM) to examine the richness patterns; GLM was used to investigate relationship between fish species richness and various environmental variables. Regression modelling involved stepwise procedures, including elimination of collinear variables, best model selection, based on the least Akaike’s information criterion (AIC) and the highest percentage of deviance explained (D²). This maiden study on the Himalayan fishes revealed that total and non-endemic fish species richness monotonously decrease with increasing elevation, while endemics peaked around mid elevations (700–1500 m). The best explanatory model (synthetic model) indicated that water discharge is the best predictor of fish species richness patterns in the Himalayan rivers.</p> <h3>Conclusions/Significance</h3><p>This study, carried out along one of the longest bioclimatic elevation gradients of the world, lends support to Rapoport’s elevational rule as opposed to mid domain effect hypothesis. We propose a species-discharge model and contradict species-area model in predicting fish species richness. We suggest that drivers of richness gradients in terrestrial and aquatic ecosystems are likely to be different. These studies are crucial in context of the impacts of unprecedented on-going river regulation on fish diversity and distribution in the Himalaya.</p> </div

Summary of the regression models between fish species richness and the Himalayan elevational gradient.

a<p>For GLM the respective best-fit polynomial order refers to a test against no relationship and with each other.</p>b<p>For GAM the respective degrees of freedom are given and refer to a test against the given GLM model.</p><p>n.s. - not significant.</p

The location of the Himalaya and study area.

<p>Upper left: The Indian Himalaya (in grey shade), and Nepal and Bhutan Himalaya. Lower left: Geographical coordinates of the Himalaya (26°30′ –37° N latitude and 72°–97°30′ E longitude). Right: Geographical coordinates of Sikkim Himalaya and location of sampling sites along Teesta river that constituted our study area.</p

Test of model robustness by cross-validation: D² values represent model fits while MAE represent mean absolute errors in number of species for the five proposed models.

<p>CV*, mean of 90 internal cross-validations (9-fold).</p

Summary statistics of linear (l) and quadratic (q) parameters of variables for selected models and resultant effects of parameter removal on model performance.

<p>SE - Standard error; <b><i>−</i></b><i>l</i> - removal of linear parameter; <b><i>−</i></b><i>q</i> - removal of quadratic parameter; n.a. - not applicable;</p><p>n.s. - not significant.</p>***<p>P<0.001;</p>**<p>P<0.01;</p>*<p>P<0.05;</p>†<p>P<0.1.</p

The increase in number of species at the uppermost 200 m elevations of the mountain passes during the last century, scaled by available habitat area.

<p>The increase in number of species at the uppermost 200 m elevations of the mountain passes during the last century, scaled by available habitat area.</p